6 citations · 12 across the 5 of their papers we have counts for
5 papers · 1 filter
Compressed Predictive Information Coding
Rui Meng, Tianyi Luo, Kristofer Bouchard
Unsupervised learning plays an important role in many fields, such as artificial intelligence, machine learning, and neuroscience. Compared to static data, methods for extracting l…
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Charles G. Frye, James Simon, Neha S. Wadia +3
Despite the fact that the loss functions of deep neural networks are highly non-convex, gradient-based optimization algorithms converge to approximately the same performance from m…
Numerically Recovering the Critical Points of a Deep Linear Autoencoder
Charles G. Frye, Neha S. Wadia, Michael R. DeWeese +1
Numerically locating the critical points of non-convex surfaces is a long-standing problem central to many fields. Recently, the loss surfaces of deep neural networks have been exp…
Optimizing the Union of Intersections LASSO () and Vector Autoregressive () Algorithms for Improved Statistical Estimation at Scale
Mahesh Balasubramanian, Trevor Ruiz, Brandon Cook +4
The analysis of scientific data of increasing size and complexity requires statistical machine learning methods that are both interpretable and predictive. Union of Intersections (…
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
Tayo Ajayi, David Mildebrath, Anastasios Kyrillidis +3
We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with -norm loss. The purpose of this work…